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Brian Guenter, Mark Finch, Steven Drucker, Desney Tan, and John Snyder.(FTC) 2018, Kohei Arai, Rahul Bhatia, and Supriya Kapoor (Eds.). Towards Reduced Latency in Saccade Landing Position Prediction Using Velocity Profile Methods. Henry Griffith, Subir Biswas, and Oleg Komogortsev.
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NVIDIA Estimates VR Is 20 Years Away From Resolutions That Match The Human Eye. Preattentive considerations for gaze-contingent image processing, Vol. Reducing visual discomfort of 3D stereoscopic sisplays with gaze-contingent depth-of-field. Wang, Krzysztof Krejtz, Izabela Krejtz, Radosław Mantiuk, and Bartosz Bazyluk. On spatiochromatic visual sensitivity and peripheral color LOD management. Duchowski, David Bate, Paris Stringfellow, Kaveri Thakur, Brian J. The Journal of Comparative Neurology 300, 1 (1990), 5-25. Topography of ganglion cells in human retina. The Laplacian Pyramid as a Compact Image Code. Retina-V1 model of detectability across the visual field. Chris Bradley, Jared Abrams, and Wilson S.A Perceptually Based Adaptive Sampling Algorithm. Contrast sensitivity of the human eye and its effects on image quality. In Human Vision, Visual Processing, and Digital Display, Vol. The square root integral (SQRI): a new metric to describe the effect of various display parameters on perceived image quality. Saccade Landing Position Prediction for Gaze-Contingent Rendering. Elena Arabadzhiyska, Okan Tarhan Tursun, Karol Myszkowski, Hans-Peter Seidel, and Piotr Didyk.Latency requirements for foveated rendering in virtual reality. Rachel Albert, Anjul Patney, David Luebke, and Joohwan Kim.Springer Berlin Heidelberg, Berlin, Heidelberg, 33-62. Hime Aguiar e Oliveira Junior, Lester Ingber, Antonio Petraglia, Mariane Rembold Petraglia, and Maria Augusta Soares Machado.
COMO SUBIR DE VERSION DE OPENGL 4.3 SERIES
We demonstrate that our predictor can efficiently drive the foveated rendering technique and analyze its benefits in a series of user experiments. This property is essential for the estimation of required quality before the full-resolution image is rendered. Its main feature is the ability to predict the parameters using only a low-resolution version of the current frame, even though the prediction holds for high-resolution rendering.
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We later use this information to derive a low-cost predictor of the foveated rendering parameters. To this end, we first study the resolution requirements at different eccentricities as a function of luminance patterns. In this work, we propose a new luminance-contrast-aware foveated rendering technique which demonstrates that the computational savings of foveated rendering can be significantly improved if local luminance contrast of the image is analyzed. Most of the current solutions model the sensitivity as a function of eccentricity, neglecting the fact that it also is strongly influenced by the displayed content. A promising solution to overcome these problems is foveated rendering, which exploits gaze information to reduce rendering quality for the peripheral vision where the requirements of the human visual system are significantly lower. Current rendering techniques struggle to fulfill quality and power efficiency requirements imposed by new display devices such as virtual reality headsets.